Method and system of detecting and recognizing a vehicle logo based on selective search

ABSTRACT

The invention discloses a method and a system of detecting and recognizing a vehicle logo based on Selective Search, the method comprising: positioning a vehicle plate on an original image of a vehicle to obtain a vehicle plate position; coarsely positioning a vehicle logo on the original image to obtain a coarse positioning image of the vehicle logo; selecting vehicle logo candidate areas in the coarse positioning image; performing target positioning in the vehicle logo candidate areas with the Selective Search to obtain a set of target regions; training a vehicle logo location classifier with Spatial Pyramid Matching based on Sparse Coding (ScSPM) to determine the vehicle logo from the set of target regions to obtain a vehicle logo position; and training a multi-class vehicle logo recognition classifier with the ScSPM to conduct a specific type-recognition for the vehicle logo to obtain a vehicle logo recognition result.

TECHNICAL FIELD

The present invention relates to the field of image processing, and moreparticularly, to a method and system of detecting and recognizing avehicle logo based on Selective Search.

BACKGROUND

A vehicle logo detecting system for vehicle is an important part of anintelligent transportation system. A vehicle logo is served as a uniquemark of vehicle brand. A vehicle recognition system can be effectivelyassisted to match with the relative information of the vehicle if thevehicle logo is correctly recognized, which will be beneficial todetermine the identity of the vehicle and improve a recognition rate ofvehicle model. Vehicle recognition is widely applied in the field ofintelligent transportation, such as vehicle model matching, vehicleinformation collection and false plate detection. Due to the differencein areas, shapes and textures of the vehicle logo in reality, variedbackground grids of the vehicle logo, and the difference in suchfeatures as the vehicle logo space position of the large vehicle andsmall vehicle, a traditional vehicle logo detecting method based ontemplate matching is difficult to obtain a high detection success ratewhile consuming a large amount of time.

In recent years, vehicle logo detection problems have been extensivelystudied. The existing vehicle logo detection algorithms include: avehicle logo detecting method based on Adaboost, a vehicle logodetecting method based on vehicle logo texture feature, a vehicle logodetecting method based on texture consistency, a vehicle logo detectingmethod based on vehicle logo background elimination, a vehicle logodetecting method based on template and a vehicle logo detecting methodbased on exhaustive search, and the like. According to the vehicle logodetecting method based on Adaboost, the Adaboost classifier is used tostudy the features of the vehicle logo and a sliding window is used toposition and recognize the vehicle logo. This method can obtain a bettervehicle logo detection result, but the time consumption is too long. Thevehicle logo detecting method based on the texture feature of thevehicle feature comprises the steps of firstly coarsely positioning thevehicle logo using the license plate position, then accuratelypositioning the vehicle logo using the prior knowledge of the vehiclelogo and the vehicle logo edge feature, and recognizing the vehicle logopositioning result using the training classifier of the support vectormachine; while the method is difficult to accurately position thevehicle logo under the condition of the complicated vehicle logobackground texture. The vehicle logo detecting method based on thetexture consistency is used for distinguishing the vehicle logo texturefeature from the vehicle logo background feature so as to accuratelyposition the vehicle logo and recognize the vehicle logo. This method isapplicable to the situation in which the vehicle logo is very differentfrom the background texture feature. But in reality, the vehicle logo isdifficult to distinguish from the background texture feature thereof.The vehicle logo detecting method based on vehicle logo backgroundelimination comprises the steps of eliminating the vehicle logobackground texture using a filtering algorithm so as to obtain theaccurate vehicle logo positioning result, then describing the vehiclelogo using SIFT and other feature descriptor and recognizing through theclassifier. This method can greatly reduce the influence of the vehiclelogo background on the vehicle logo positioning, but it is easy toeliminate the vehicle logo information and affects the vehicle logopositioning and recognizing result. The vehicle logo detecting methodbased on template matching comprises the step of positing the positionand the recognition result of the vehicle logo by taking the vehiclelogo as the template. This method is relatively affected by the vehiclelogo background little, but the time consumption is too long. Thevehicle logo detecting method based on exhaustive search comprises thesteps of performing the exhaustive search on the target area using thesliding window, and judging the target for the vehicle logo using suchHOG SIFT and other descriptor to obtain the vehicle logo area and thevehicle logo type. This method is theoretically applicable to allmodels, but the sliding window is relatively slow, which greatly affectsthe practicability of the algorithm.

In conclusion, the current vehicle logo detection algorithms have thefollowing shortcomings or deficiencies that:

-   -   1) the vehicle logo for small vehicle is mostly detected merely,        so that it is unable to be applied to all models, and the        applicability is not wide.    -   2) under the influence of illumination, inclination and        complicated grid background of vehicle logo, the current        algorithm is difficult to detect the vehicle logo accurately,        and the robustness is weaker.    -   3) the consumption of time is long, the detection speed is slow,        so that it is unable to meet the actual requirements of the high        detection speed.

SUMMARY

In order to solve the above technical problems, an object of theinvention is to provide a vehicle logo detecting and recognizing methodbased on Selective Search having wide applicability, strong robustnessand fast detection speed.

Another object of the invention is to provide a vehicle logo detectingand recognizing system based on Selective Search having wideapplicability, strong robustness and fast detection speed.

A technical solution adopted by the invention to achieve the method andsystem can be described as follows.

A method of detecting and recognizing vehicle logo based on SelectiveSearch, comprising:

-   -   positioning a license plate on an original image of a vehicle to        obtain a license plate position;    -   coarsely positioning a vehicle logo on the original image of the        vehicle according to the license plate position, spatial        structure between the license plate and the vehicle logo, and        vehicle window edge feature, to obtain a coarse positioning        image of the vehicle logo;    -   selecting vehicle logo candidate areas in the coarse positioning        image of the vehicle logo based on a central axis of the        vehicle;    -   performing target positioning in the vehicle logo candidate        areas with Selective Search, to obtain a set of target regions,        and performing region combination with the Selective Search        based on Color similarity, Texture similarity, Size similarity        and Fill similarity comprehensively;    -   training a vehicle logo location classifier with Spatial Pyramid        Matching based on Sparse Coding (ScSPM), to determine the        vehicle logo from the set of target regions, to obtain a vehicle        logo position; and    -   training a multi-class vehicle logo recognition classifier with        the Spatial Pyramid Matching based on Sparse Coding (ScSPM) to        conduct a specific type-recognition for the vehicle logo, to        obtain a vehicle logo recognition result.

Further, the step of coarsely positioning a vehicle logo on the originalimage of the vehicle according to the license plate position, spatialstructure between the license plate and the vehicle logo, and vehiclewindow edge feature, to obtain a coarse positioning image of the vehiclelogo, comprises the following sub-steps of:

-   -   determining a position of a down boundary for vehicle logo        coarse positioning according to a position of a up boundary for        vehicle logo coarse positioning, the down boundary for vehicle        logo coarse positioning having a coordinate Y_(down) determined        by a formula Y_(down)=y_(up), wherein y_(up) refers to a        coordinate of the up boundary for vehicle logo coarse        positioning;    -   coarsely positioning a vehicle window according to vertical        projection of the original image of the vehicle to obtain the        vehicle window edge feature, and determining a position of the        up boundary for vehicle logo coarse positioning according to the        vehicle window edge feature, the coordinate Y_(up) of the up        boundary for the vehicle logo coarse positioning being        determined by a formula:

$\left\{ {\begin{matrix}{Y_{up} = {x_{2} - {\left( {x_{2} - x_{1}} \right)/2}}} \\{x_{1},{x_{2} = {\max_{2}{h(x)}}}} \\{{{s.t.\mspace{14mu} {{x_{1} - x_{2}}}} \in \left\lbrack {{H/4},{H/2}} \right\rbrack},{x_{1} \in \left( {0,{H/3}} \right\rbrack},{{{{h\left( x_{1} \right)} - {h\left( x_{2} \right)}}} \leq b}}\end{matrix},} \right.$

-   -   wherein, h(x) refers to the vertical projection of the original        image of the vehicle, max₂ h(x) denotes x-coordinates x₁ and x₂        corresponding to two maximum values h(x₁) and h(x₂) selected        from the vertical projection h(x) of the edge from up to down, H        refers to a height of the original image of the vehicle, and b        refers to an experience threshold value; and    -   obtaining the coarse positioning image of the vehicle logo        according to the coordinate Y_(down) of down boundary for        vehicle logo coarse positioning and the coordinate Y_(up) of the        up boundary for vehicle logo coarse positioning.

Further, the step of selecting vehicle logo candidate areas in thecoarse positioning image of the vehicle logo based on a central axis ofvehicle, comprises the following sub-steps of:

-   -   determining the central axis of the vehicle on the coarse        positioning image of the vehicle logo; and    -   selecting an area having a set width and a set height as a first        vehicle logo candidate area according to the central axis of the        vehicle.

Further, the step of performing target positioning in the vehicle logocandidate areas with the Selective Search, to obtain a set of targetregions, comprises the following sub-steps of:

-   -   S1. performing target detection in the first vehicle logo        candidate area using the Selective Search, going to step S2 if        falling to detect any target, otherwise, directly jumping to        step S3;    -   S2. transversely amplifying the first vehicle logo candidate        area with a transversely amplified width set to form a second        vehicle logo candidate area, and then performing target        detection for vehicle logo in the second vehicle logo candidate        area using the Selective Search, if still failing to detect any        target, abandoning the first vehicle logo candidate area and        continuing to amplify the second vehicle logo candidate area        with a new transversely amplified width, until a target is        detected in the second vehicle logo candidate area, then going        to step S3; otherwise, going to step S3; and    -   S3. determine the target detected is a single character or a        character string vehicle logo or a symbol type vehicle logo,        ending the Selective Search if the target detected is the        character string vehicle logo or the symbol type vehicle logo,        taking 1.5 times of the character height as a height of the        candidate region, i.e. a height of the third vehicle logo        candidate area if the target detected is the single character,        and continuing to perform target detection in the third vehicle        logo candidate area using the Selective Search.

Further, the step of performing target positioning in the vehicle logocandidate areas with the Selective Search to obtain a set of targetregions, comprises the following sub-steps of:

-   -   obtaining initial target regions from the vehicle logo candidate        areas using an image segmentation algorithm based on a graph        theory;    -   calculating comprehensive similarity between the adjacent        regions in the initial target regions, via a calculation        formula:

$\left\{ {\begin{matrix}{{s\left( {r_{i},r_{j}} \right)} = {{a_{1}{s_{color}\left( {r_{i},r_{j}} \right)}} + {a_{2}s_{texture}\left( {r_{i},r_{j}} \right)} + {a_{3}{s_{size}\left( {r_{i},r_{j}} \right)}} + {a_{4}{{fill}\left( {r_{i},r_{j}} \right)}}}} \\\begin{matrix}{{{s_{color}\left( {r_{i},r_{j}} \right)} = {\sum\limits_{k = 1}^{n}{\min \left( {c_{i}^{k},c_{j}^{k}} \right)}}},{{c_{i}^{k} \in C_{i}} = \left\{ {c_{i}^{1},\ldots \mspace{14mu},c_{i}^{n}} \right)},{{c_{j}^{k} \in C_{j}} =}} \\\left\{ {c_{j}^{1},\ldots \mspace{14mu},c_{j}^{n}} \right\}\end{matrix} \\\begin{matrix}{{{s_{texture}\left( {r_{i},r_{j}} \right)} = {\sum\limits_{k = 1}^{n}{\min \left( {t_{i}^{k},t_{j}^{k}} \right)}}},{{t_{i}^{k} \in T_{i}} = \left\{ {c_{i}^{1},\ldots \mspace{14mu},c_{i}^{n}} \right\}},{{t_{j}^{k} \in T_{j}} =}} \\\left\{ {c_{j}^{1},\ldots \mspace{14mu},c_{j}^{n}} \right\}\end{matrix} \\{{s_{size}\left( {r_{i},r_{j}} \right)} = {1 - \frac{{{size}\left( r_{i} \right)} + {{size}\left( r_{j} \right)}}{{size}({im})}}} \\{{{fill}\left( {r_{i},r_{j}} \right)} = {1 - \frac{{{size}\left( {BB}_{ij} \right)} - {{size}\left( r_{i} \right)} - {{size}\left( r_{j} \right)}}{{size}({im})}}}\end{matrix};} \right.$

-   -   wherein s(r_(i),r_(j)), s_(color)(r_(i),r_(j)),        s_(texture)(r_(i),r_(j)), s_(size)(r_(i),r_(j)) and        fill(r_(i),r_(j)) refer to comprehensive similarity, Color        similarity, Texture similarity, Size similarity and Fill        similarity respectively between a region r_(i) and a region        r_(j), a₁, a₂, a₃ and a₄ refer to a Color similarity weighting        coefficient, a Texture similarity weighting coefficient, a Size        similarity weighting coefficient and a Fill similarity weighting        coefficient respectively, the value range of a₁, a₂, a₃ and a₄        is within (0, 1), C_(i)={c_(i) ¹, . . . , c_(i) ^(n)} and        C_(j)={c_(j) ¹, . . . , c_(j) ^(n)} refer to 3×25-dimensional        color space vectors corresponding to the region r_(i) and the        region r_(j) respectively, T_(i)={t_(i) ¹, . . . , t_(i) ^(n)}        and T_(j)={t_(j) ¹, . . . , t_(j) ^(n)} refer to        8×3×10-dimensional texture vector corresponding to the region        and the region respectively, n refers to the total number of        elements of the color space vector or texture vector, and        size(r_(i)), size(r_(j)), size(im) and size(BB_(ij)) refer to        size of the region r_(i), size of the region r_(j), size of the        whole image combined by all the regions, and size of a outer        boundary rectangle of a region combined by the region r_(i) and        the region r_(j), respectively; and    -   taking the comprehensive similarity maximum as a combination        principle, combining the initial target regions according to the        comprehensive similarity calculated between the adjacent        regions, to obtain the set of target regions.

Further, the step of training a vehicle logo location classifier withSpatial Pyramid Matching based on Sparse Coding (ScSPM), to determinethe vehicle logo from the set of target regions, to obtain a vehiclelogo position, comprises the following sub-steps of:

-   -   dividing sample images into positive samples and negative        samples, wherein the positive samples comprises single character        samples, and small-vehicle logo samples and large-vehicle logo        samples in the sample set, and the negative sample are samples        having intersection over union (IoU) less than 20% with the        vehicle logo and randomly selected from the sample set in size;    -   taking the positive samples as training samples, and using the        ScSPM to perform training iteratively until convergence, and        finally to have the vehicle logo location classifier, wherein,        in the iterative training process, after each training, the        samples which are wrongly divided into the negative samples in        the vehicle logo location classifier is added into the training        samples to form a new training sample set, then the new training        sample set is used for retraining; and    -   determining the vehicle logo from the set of target regions        according to the vehicle logo location classified trained, to        obtain the position of the vehicle logo.

Further, the step of training a multi-class vehicle logo recognitionclassifier with the ScSPM to conduct a specific type-recognition for thevehicle logo, to obtain a vehicle logo recognition result, comprises thefollowing sub-steps of:

-   -   taking single characters as vehicle logos, feeding the single        characters of manually annotated vehicle logos and the character        string vehicle logos into the ScSPM to perform training        iteratively, and taking the wrongly-classified vehicle logos as        hard examples and feeding them into the ScSPM for training until        convergence, to obtain the multi-class vehicle logo recognition        classifier; and    -   performing specific type-recognition for the vehicle logo with        the multi-class vehicle logo recognition classifier: if the        vehicle logo recognition result of the current multi-class        vehicle logo recognition classifier is letter logo or symbol,        taking the vehicle logo recognition result of the current        multi-class vehicle logo recognition classifier as the vehicle        logo type of the vehicle; if the vehicle logo recognition result        of the current multi-class vehicle logo recognition classifier        is a single character, reselecting a third vehicle logo        candidate area according to the single character and        re-positioning the vehicle logo, and combining the characters        obtained by positioning to form the character string, and        finally taking the vehicle logo type of the character string as        the vehicle logo recognition result.        The invention adopts another technical solution as follows:        A system of detecting and recognizing a vehicle logo based on        Selective Search, comprising:    -   a license plate positioning module, configured to position a        license plate on an original image of a vehicle, to obtain a        license plate position;    -   a vehicle logo coarsely positioning module, configured to        coarsely position a vehicle logo on the original image of the        vehicle according to the license plate position, spatial        structure between the license plate and the vehicle logo, and        vehicle window edge feature, to obtain a coarse positioning        image of the vehicle logo;    -   a vehicle logo candidate area selecting module, configured to        select vehicle logo candidate areas in the coarse positioning        image of the vehicle logo based on a central axis of the        vehicle;    -   a target positioning module, configured to perform target        positioning in the vehicle logo candidate areas with the        Selective Search, to obtain a set of target regions, and        performing region combination with the Selective Search based on        Color similarity, Texture similarity, Size similarity and Fill        similarity comprehensively;    -   a vehicle logo determining module, configured to train a vehicle        logo location classifier with the ScSPM, to determine the        vehicle logo from the set of target regions, to obtain a vehicle        logo position; and    -   a vehicle logo type recognition module, configured to train a        multi-class vehicle logo recognition classifier with the ScSPM        to conduct a specific type-recognition for the vehicle logo, to        obtain a vehicle logo recognition result.

Further, the vehicle logo coarse positioning module comprises:

-   -   a vehicle logo coarse positioning down boundary position        determining unit, configured to determine a position of a down        boundary for vehicle logo coarse positioning according to a        position of a up boundary for vehicle logo coarse positioning,        the down boundary for vehicle logo coarse positioning having a        coordinate Y_(down) determined by a formula Y_(down)=y_(up),        wherein y_(up) refers to a coordinate of the up boundary for        vehicle logo coarse positioning;    -   a vehicle logo coarse positioning up boundary position        determining unit, configured to coarsely position a vehicle        window according to vertical projection of the original image of        the vehicle to gain the vehicle window edge feature, and        determining a position of the up boundary for vehicle logo        coarse positioning according to the vehicle window edge feature,        the coordinate Y_(up) of the up boundary for the vehicle logo        coarse positioning being determined by a formula:

$\left\{ {\begin{matrix}{Y_{up} = {x_{2} - {\left( {x_{2} - x_{1}} \right)/2}}} \\{x_{1},{x_{2} = {\max_{2}{h(x)}}}} \\{{{s.t.\mspace{14mu} {{x_{1} - x_{2}}}} \in \left\lbrack {{H/4},{H/2}} \right\rbrack},{x_{1} \in \left( {0,{H/3}} \right\rbrack},{{{{h\left( x_{1} \right)} - {h\left( x_{2} \right)}}} \leq b}}\end{matrix},} \right.$

-   -   wherein, h(x) refers to the vertical projection of the original        image of the vehicle, max₂ h(x) denotes x-coordinates x₁ and x₂        corresponding to two maximum values h(x₁) and selected from the        vertical projection h(x) of the edge from up to down, H refers        to a height of the original image of the vehicle, and b refers        to an experience threshold value; and    -   a vehicle logo coarse positioning image obtaining unit,        configured to obtain the coarse positioning image of the vehicle        logo according to the coordinate Y_(down) of down boundary for        vehicle logo coarse positioning and the coordinate Y_(up) of the        up boundary for vehicle logo coarse positioning.

Further, the target positioning module comprises:

-   -   an image segmentation unit, configured to obtain initial target        regions from the vehicle logo candidate areas using an image        segmentation algorithm based on a graph theory; a similarity        calculation unit, configured to calculate comprehensive        similarity between the adjacent regions in the initial target        regions, via a calculation formula:

$\left\{ {\begin{matrix}{{s\left( {r_{i},r_{j}} \right)} = {{a_{1}{s_{color}\left( {r_{i},r_{j}} \right)}} + {a_{2}{s_{texture}\left( {r_{i},r_{j}} \right)}} + {a_{3}{s_{size}\left( {r_{i},r_{j}} \right)}} + {a_{4}{{fill}\left( {r_{i},r_{j}} \right)}}}} \\{{s_{color}\left( {r_{i},r_{j}} \right)} =} \\{{\sum\limits_{k = 1}^{n}{\min \left( {c_{i}^{k},c_{j}^{k}} \right)}},{{c_{i}^{k} \in C_{i}} = \left\{ {c_{i}^{1},\ldots \mspace{14mu},c_{i}^{n}} \right)},{{c_{j}^{k} \in C_{j}} = \left\{ {c_{j}^{1},\ldots \mspace{14mu},c_{j}^{n}} \right\}}} \\{{s_{texture}\left( {r_{i},r_{j}} \right)} =} \\{{\sum\limits_{k = 1}^{n}{\min \left( {t_{i}^{k},t_{j}^{k}} \right)}},{{t_{i}^{k} \in T_{i}} = \left\{ {c_{i}^{1},\ldots \mspace{14mu},c_{i}^{n}} \right\}},{{t_{j}^{k} \in T_{j}} = \left\{ {c_{j}^{1},\ldots \mspace{14mu},c_{j}^{n}} \right\}}} \\{{s_{size}\left( {r_{i},r_{j}} \right)} = {1 - \frac{{{size}\left( r_{i} \right)} + {{size}\left( r_{j} \right)}}{{size}({im})}}} \\{{{fill}\left( {r_{i},r_{j}} \right)} = {1 - \frac{{{size}\left( {BB}_{ij} \right)} - {{size}\left( r_{i} \right)} - {{size}\left( r_{j} \right)}}{{size}({im})}}}\end{matrix};} \right.$

Wherein s(r_(i),r_(j)), s_(color)(r_(i),r_(j)),s_(texture)(r_(i),r_(j)), s_(size)(r_(i),r_(j)) and fill(r_(i),r_(j))refer to comprehensive similarity, Color similarity, Texture similarity,Size similarity and Fill similarity respectively between a region and aregion r_(i) and a region r_(j), a₁, a₂, a₃ and a₄ refer to a Colorsimilarity weighting coefficient, a Texture similarity weightingcoefficient, a Size similarity weighting coefficient and a Fillsimilarity weighting coefficient respectively, the value range of a₁,a₂, a₃ and a₄ is within (0, 1), C_(i)={c_(i) ¹, . . . , c_(i) ^(n)} andC_(j)={c_(j) ¹, . . . , c_(j) ^(n)} refer to 3×25-dimensional colorspace vectors corresponding to the region r_(i) and the region r_(j)respectively, T_(i)={t_(i) ¹, . . . , t_(i) ^(n)} and T_(j)={t_(j) ¹, .. . , t_(j) ^(n)} refer to 8×3×10-dimensional texture vectorcorresponding to the region r_(i) and the region r_(j) respectively, nrefers to the total number of elements of the color space vector ortexture vector, and size(r_(i)), size(r_(j)), size(im) and size(BB_(ij))refer to size of the region r_(i), size of the region r_(j), size of thewhole image combined by all the regions, and size of a outer boundaryrectangle of a region combined by the region r_(i) and the region r_(j),respectively; and

-   -   a region combination unit, configured to combine the initial        target regions according to the comprehensive similarity        calculated between the adjacent regions to obtain the set of        target regions, taking the comprehensive similarity maximum as a        combination principle.

The method of the present invention has the following beneficialeffects: firstly, coarsely positioning the vehicle logo according to thespace structure between the license plate and the vehicle log and thevehicle window edge feature, obtaining the vehicle logo candidate areabased on the central axis of the vehicle, then positioning the targetusing the Selective Search, and finally distinguishing, screening andrecognizing the target by the Spatial Pyramid Matching based on SparseCoding (ScSPM) to obtain the vehicle logo position and the vehicle logorecognition result, without the limitation of the vehicle model, boththe large vehicles and the small vehicles being able to be subject tothe vehicle logo detection and recognition, with wide application; usingthe Selective Search, comprehensively combining the region according tothe Color similarity, Texture similarity, Size similarity and Fillsimilarity, being still able to correctly detecting the position of thevehicle logo by the texture, color, size and fitness features of thevehicle logo under the influence of illumination, inclination andcomplicated grid background of vehicle logo, with strong robustness; andselecting the vehicle logo candidate area according to the spaceposition information of the vehicle and the central axis of the vehicle,greatly reducing the time-consumption of the Selective Search, andhaving a faster detection speed. Further, the Selective Search uses aTexture similarity calculating method based on a histogram of orientedgradients, and uses the histogram of oriented gradients to replace ascale invariant transform feature, which can greatly reduce theconsumption of time while ensuring the vehicle logo positioning accuracyrate and recognition accuracy rate.

The system of the present invention has the advantageous effects:firstly, coarsely positioning the vehicle logo according to the spacestructure between the license plate and the vehicle log and the vehiclewindow edge feature in the vehicle logo coarse positioning module,obtaining the vehicle logo candidate area based on the central axis ofthe vehicle in the vehicle logo candidate area, then positioning thetarget using the Selective Search, and finally distinguishing in thetarget positioning module, screening and recognizing the target by theScSPM to obtain the vehicle logo position and the vehicle logorecognition result in the vehicle logo judgment module and the vehiclelogo type recognition module, without the limitation of the vehiclemodel, both the large vehicle and the small vehicle being able to besubject to the vehicle logo detection and recognition, with wideapplication; using the Selective Search in the target positioningmodule, comprehensively combining the region according to the Colorsimilarity, Texture similarity, Size similarity and Fill similarity,being still able to correctly detecting the position of the vehicle logoby the texture, color, size and fitness features of the vehicle logounder the influence of illumination, inclination and complicated gridbackground of vehicle logo, with strong robustness; and selecting thevehicle logo candidate area according to the space position informationof the vehicle and the central axis of the vehicle in the vehicle logocoarse positioning module and the vehicle logo candidate area, greatlyreducing the time-consumption of the Selective Search, and having afaster detection speed. Further, the Selective Search of the targetpositioning module uses a Texture similarity calculating method based ona histogram of oriented gradients, and uses the histogram of orientedgradients to replace a scale invariant transform feature, which cangreatly reduce the consumption of time while ensuring the vehicle logopositioning accuracy rate and recognition accuracy rate.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an overall flow chart of a method of detecting and recognizinga vehicle logo based on Selective Search according to the invention;

FIG. 2A is a schematic diagram of a first type of vehicle logo of alarge vehicle;

FIG. 2B is a schematic diagram of a second type of vehicle logo of alarge vehicle;

FIG. 2C is a schematic diagram of a third type of vehicle logo of alarge vehicle;

FIG. 2D is a schematic diagram of a fourth type of vehicle logo of alarge vehicle;

FIG. 3A is a schematic diagram of a coarse positioning process of thevehicle logo according to the invention;

FIG. 3B is an additional schematic diagram of a coarse positioningprocess of the vehicle logo according to the invention;

FIG. 3C is another schematic diagram of a coarse positioning process ofthe vehicle logo according to the invention;

FIG. 4A is a schematic diagram of a designating process of a vehiclelogo candidate area according to the invention;

FIG. 4B is an additional schematic diagram of a designating process of avehicle logo candidate area according to the invention;

FIG. 5 is a flow chart of target positioning for the vehicle logocandidate area using the Selective Search according to the invention;

FIG. 6 is a schematic diagram of a training process of a vehicle logojudgment classifier;

FIG. 7 is a schematic diagram of a training process of a multi-classvehicle logo recognition classifier; and

FIG. 8 is a chart diagram of a vehicle logo recognition processaccording to the invention.

DETAILED DESCRIPTION

Referring to FIG. 1, a method of detecting and recognizing a vehiclelogo based on Selective Search according to the invention comprises thefollowing steps of:

A method of detecting and recognizing a vehicle logo based on SelectiveSearch, comprising:

-   -   positioning a license plate on an original image of a vehicle to        obtain a license plate position;    -   coarsely positioning a vehicle logo on the original image of the        vehicle according to the license plate position, spatial        structure between the license plate and the vehicle logo, and        vehicle window edge feature, to obtain a coarse positioning        image of the vehicle logo;    -   selecting vehicle logo candidate areas in the coarse positioning        image of the vehicle logo based on a central axis of the        vehicle;    -   performing target positioning in the vehicle logo candidate        areas with the Selective Search, to obtain a set of target        regions, and performing region combination with the Selective        Search based on Color similarity, Texture similarity, Size        similarity and Fill similarity comprehensively;    -   training a vehicle logo location classifier with Spatial Pyramid        Matching based on Sparse Coding (ScSPM), to determine the        vehicle logo from the set of target regions, to obtain a vehicle        logo position; and    -   training a multi-class vehicle logo recognition classifier with        the ScSPM to conduct a specific type-recognition for the vehicle        logo, to obtain a vehicle logo recognition result.

Further, as a preferred embodiment, the step of coarsely positioning avehicle logo on the original image of the vehicle according to thelicense plate position, spatial structure between the license plate andthe vehicle logo, and vehicle window edge feature, to obtain a coarsepositioning image of the vehicle logo, comprises the following sub-stepsof

-   -   determining a position of a down boundary for vehicle logo        coarse positioning according to a position of a up boundary for        vehicle logo coarse positioning, the down boundary for vehicle        logo coarse positioning having a coordinate Y_(down) determined        by a formula Y_(down)=y_(up), wherein y_(up) refers to a        coordinate of the up boundary for vehicle logo coarse        positioning;    -   coarsely positioning a vehicle window according to vertical        projection of the original image of the vehicle to gain the        vehicle window edge feature, and determining a position of the        up boundary for vehicle logo coarse positioning according to the        vehicle window edge feature, the coordinate Y_(up) of the up        boundary for the vehicle logo coarse positioning being        determined by a formula:

$\left\{ {\begin{matrix}{Y_{up} = {x_{2} - {\left( {x_{2} - x_{1}} \right)/2}}} \\{x_{1},{x_{2} = {\max_{2}{h(x)}}}} \\{{{s.t.\mspace{14mu} {{x_{1} - x_{2}}}} \in \left\lbrack {{H/4},{H/2}} \right\rbrack},{x_{1} \in \left( {0,{H/3}} \right\rbrack},{{{{h\left( x_{1} \right)} - {h\left( x_{2} \right)}}} \leq b}}\end{matrix},} \right.$

-   -   wherein, refers to the vertical projection of the original image        of the vehicle, max₂ h(x) denotes x-coordinates x₁ and x₂        corresponding to two maximum values h(x₁) and h(x₂) selected        from the vertical projection (x) of the edge from up to down, H        refers to a height of the original image of the vehicle, and b        refers to an experience threshold value; and    -   obtaining the coarse positioning image of the vehicle logo        according to the coordinate Y_(down) of down boundary for        vehicle logo coarse positioning and the coordinate Y_(up) of the        up boundary for vehicle logo coarse positioning.

Further, as a preferred embodiment, the step of selecting vehicle logocandidate areas in the coarse positioning image of the vehicle logobased on a central axis of vehicle, comprises the following sub-stepsof:

-   -   determining the central axis of the vehicle on the coarse        positioning image of the vehicle logo; and    -   selecting an area having a set width and a set height as a first        vehicle logo candidate area according to the central axis of the        vehicle.

Further, as a preferred embodiment, the step of performing targetpositioning in the vehicle logo candidate areas with the SelectiveSearch, to obtain a set of target regions, comprises the followingsub-steps of:

-   -   S1. performing target detection in the first vehicle logo        candidate area using the Selective Search, going to step S2 if        falling to detect any target, otherwise, directly jumping to        step S3;    -   S2. transversely amplifying the first vehicle logo candidate        area with a transversely amplified width set to form a second        vehicle logo candidate area, and then performing target        detection for vehicle logo in the second vehicle logo candidate        area using the Selective Search, if still failing to detect any        target, abandoning the first vehicle logo candidate area and        continuing to amplify the second vehicle logo candidate area        with a new transversely amplified width, until a target is        detected in the second vehicle logo candidate area, then going        to step S3; otherwise, going to step S3; and    -   S3. determine the target detected is a single character or a        character string vehicle logo or a symbol type vehicle logo,        ending the Selective Search if the target detected is the        character string vehicle logo or the symbol type vehicle logo,        taking 1.5 times of the character height as a height of the        candidate region, i.e. a height of the third vehicle logo        candidate area if the target detected is the single character,        and continuing to perform target detection in the third vehicle        logo candidate area using the Selective Search.

Further, as a preferred embodiment, the step of performing targetpositioning in the vehicle logo candidate areas with the SelectiveSearch to obtain a set of target regions, comprises the followingsub-steps of:

-   -   obtaining initial target regions from the vehicle logo candidate        areas using an image segmentation algorithm based on a graph        theory;    -   calculating comprehensive similarity between the adjacent        regions in the initial target regions, via a calculation        formula:

$\left\{ {\begin{matrix}{{s\left( {r_{i},r_{j}} \right)} =} \\{{a_{1}{s_{color}\left( {r_{i},r_{j}} \right)}} + {a_{2}{s_{texture}\left( {r_{i},r_{j}} \right)}} + {a_{3}{s_{size}\left( {r_{i},r_{j}} \right)}} + {a_{4}{{fill}\left( {r_{i},r_{j}} \right)}}} \\{{s_{color}\left( {r_{i},r_{j}} \right)} =} \\{{\sum\limits_{k = 1}^{n}{\min \left( {c_{i}^{k},c_{j}^{k}} \right)}},{{c_{i}^{k} \in C_{i}} = \left\{ {c_{i}^{1},\ldots \mspace{14mu},c_{i}^{n}} \right)},{{c_{j}^{k} \in C_{j}} = \left\{ {c_{j}^{1},\ldots \mspace{14mu},c_{j}^{n}} \right\}}} \\{{s_{texture}\left( {r_{i},r_{j}} \right)} =} \\{{\sum\limits_{k = 1}^{n}{\min \left( {t_{i}^{k},t_{j}^{k}} \right)}},{{t_{i}^{k} \in T_{i}} = \left\{ {c_{i}^{1},\ldots \mspace{14mu},c_{i}^{n}} \right\}},{{t_{j}^{k} \in T_{j}} = \left\{ {c_{j}^{1},\ldots \mspace{14mu},c_{j}^{n}} \right\}}} \\{{s_{size}\left( {r_{i},r_{j}} \right)} = {1 - \frac{{{size}\left( r_{i} \right)} + {{size}\left( r_{j} \right)}}{{size}({im})}}} \\{{{fill}\left( {r_{i},r_{j}} \right)} = {1 - \frac{{{size}\left( {BB}_{ij} \right)} - {{size}\left( r_{i} \right)} - {{size}\left( r_{j} \right)}}{{size}({im})}}}\end{matrix};} \right.$

-   -   wherein s(r_(i),r_(j)), s_(color)(r_(i),r_(j)),        s_(texture)(r_(i),r_(j)), s_(size)(r_(i),r_(j)) and fill        (r_(i),r_(j)) refer to comprehensive similarity, Color        similarity, Texture similarity, Size similarity and Fill        similarity respectively between a region r_(i) and a region        r_(j), a₁, a₂, a₃ and a₄ refer to a Color similarity weighting        coefficient, a Texture similarity weighting coefficient, a Size        similarity weighting coefficient and a Fill similarity weighting        coefficient respectively, the value range of a₁, a₂, a₃ and a₄        is within (0, 1), C_(i)={c_(i) ¹, . . . , c_(i) ^(n)} and        C_(j)={c_(j) ¹, . . . , c_(j) ^(n)} refer to 3×25-dimensional        color space vectors corresponding to the region r_(i) and the        region r_(j) respectively, T_(i)={t_(i) ¹, . . . , t_(i) ^(n)}        and T_(j)={t_(j) ¹, . . . , t_(j) ^(n)} refer to        8×3×10-dimensional texture vector corresponding to the region        r_(i) and the region r_(j) respectively, n refers to the total        number of elements of the color space vector or texture vector,        and size(r_(i)), size(r_(j)), size(im) and size(BB_(ij)) refer        to size of the region r_(i), size of the region r_(j), size of        the whole image combined by all the regions, and size of a outer        boundary rectangle of a region combined by the region r_(i) and        the region r_(j), respectively; and    -   taking the comprehensive similarity maximum as a combination        principle, combining the initial target regions according to the        comprehensive similarity calculated between the adjacent        regions, to obtain the set of target regions.

Further, as a preferred embodiment, the step of training a vehicle logolocation classifier with ScSPM, to determine the vehicle logo from theset of target regions, to obtain a vehicle logo position, comprises thefollowing sub-steps of:

-   -   dividing sample images into positive samples and negative        samples, wherein the positive samples comprises single character        samples, and small-vehicle logo samples and large-vehicle logo        samples in the sample set, and the negative sample are samples        having intersection over union (IoU) less than 20% with the        vehicle logo and randomly selected from the sample set in size;    -   taking the positive samples as training samples, and using the        ScSPM to perform training iteratively until convergence, and        finally to have the vehicle logo location classifier, wherein,        in the iterative training process, after each training, the        samples which are wrongly divided into the negative samples in        the vehicle logo location classifier is added into the training        samples to form a new training sample set, then the new training        sample set is used for retraining; and    -   determining the vehicle logo from the set of target regions        according to the vehicle logo location classified trained, to        obtain the position of the vehicle logo.

Further, as a preferred embodiment, the step of training a multi-classvehicle logo recognition classifier with the ScSPM to conduct a specifictype-recognition for the vehicle logo, to obtain a vehicle logorecognition result, comprises the following sub-steps of:

-   -   taking single characters as vehicle logos, feeding the single        characters of manually annotated vehicle logos and the character        string vehicle logos into the ScSPM to perform training        iteratively, and taking the wrongly-classified vehicle logos as        hard examples and feeding them into the ScSPM for training until        convergence, to obtain the multi-class vehicle logo recognition        classifier; and    -   performing specific type-recognition for the vehicle logo with        the multi-class vehicle logo recognition classifier: if the        vehicle logo recognition result of the current multi-class        vehicle logo recognition classifier is letter logo or symbol,        taking the vehicle logo recognition result of the current        multi-class vehicle logo recognition classifier as the vehicle        logo type of the vehicle; if the vehicle logo recognition result        of the current multi-class vehicle logo recognition classifier        is a single character, reselecting a third vehicle logo        candidate area according to the single character and        re-positioning the vehicle logo, and combining the characters        obtained by positioning to form the character string, and        finally taking the vehicle logo type of the character string as        the vehicle logo recognition result.

Referring to FIG. 1 again, a system of detecting and recognizing avehicle logo based on Selective Search, comprises:

-   -   a license plate positioning module, configured to position a        license plate on an original image of a vehicle, to obtain a        license plate position;    -   a vehicle logo coarsely positioning module, configured to        coarsely position a vehicle logo on the original image of the        vehicle according to the license plate position, spatial        structure between the license plate and the vehicle logo, and        vehicle window edge feature, to obtain a coarse positioning        image of the vehicle logo;    -   a vehicle logo candidate area selecting module, configured to        select vehicle logo candidate areas in the coarse positioning        image of the vehicle logo based on a central axis of the        vehicle;    -   a target positioning module, configured to perform target        positioning in the vehicle logo candidate areas with the        Selective Search, to obtain a set of target regions, and        performing region combination with the Selective Search based on        Color similarity, Texture similarity, Size similarity and Fill        similarity comprehensively;    -   a vehicle logo determining module, configured to train a vehicle        logo location classifier with Spatial Pyramid Matching based on        Sparse Coding (ScSPM), to determine the vehicle logo from the        set of target regions, to obtain a vehicle logo position; and    -   a vehicle logo type recognition module, configured to train a        multi-class vehicle logo recognition classifier with the ScSPM        to conduct a specific type-recognition for the vehicle logo, to        obtain a vehicle logo recognition result.

Further, as a preferred embodiment, the vehicle logo coarse positioningmodule comprises:

-   -   a vehicle logo coarse positioning down boundary position        determining unit, configured to determine a position of a down        boundary for vehicle logo coarse positioning according to a        position of a up boundary for vehicle logo coarse positioning,        the down boundary for vehicle logo coarse positioning having a        coordinate Y_(down) determined by a formula Y_(down)=y_(up),        wherein y_(up) refers to a coordinate of the up boundary for        vehicle logo coarse positioning;    -   a vehicle logo coarse positioning up boundary position        determining unit, configured to coarsely position a vehicle        window according to vertical projection of the original image of        the vehicle to gain the vehicle window edge feature, and        determining a position of the up boundary for vehicle logo        coarse positioning according to the vehicle window edge feature,        the coordinate Y_(up) of the up boundary for the vehicle logo        coarse positioning being determined by a formula:

$\left\{ {\begin{matrix}{Y_{up} = {x_{2} - {\left( {x_{2} - x_{1}} \right)/2}}} \\{x_{1},{x_{2} = {\max_{2}{h(x)}}}} \\{{{s.t.\mspace{14mu} {{x_{1} - x_{2}}}} \in \left\lbrack {{H/4},{H/2}} \right\rbrack},{x_{1} \in \left( {0,{H/3}} \right\rbrack},{{{{h\left( x_{1} \right)} - {h\left( x_{2} \right)}}} \leq b}}\end{matrix},} \right.$

-   -   wherein, h(x) refers to the vertical projection of the original        image of the vehicle, max₂ h(x) denotes x-coordinates x₁ and x₂        corresponding to two maximum values h(x₁) and h(x₂) selected        from the vertical projection h(x) of the edge from up to down, H        refers to a height of the original image of the vehicle, and b        refers to an experience threshold value; and    -   a vehicle logo coarse positioning image obtaining unit,        configured to obtain the coarse positioning image of the vehicle        logo according to the coordinate Y_(down) of down boundary for        vehicle logo coarse positioning and the coordinate Y_(up) of the        up boundary for vehicle logo coarse positioning.

Further, as a preferred embodiment, the target positioning modulecomprises:

-   -   an image segmentation unit, configured to obtain initial target        regions from the vehicle logo candidate areas using an image        segmentation algorithm based on a graph theory;    -   a similarity calculation unit, configured to calculate        comprehensive similarity between the adjacent regions in the        initial target regions, via a calculation formula:

$\left\{ {\begin{matrix}{{s\left( {r_{i},r_{j}} \right)} =} \\{{a_{1}{s_{color}\left( {r_{i},r_{j}} \right)}} + {a_{2}{s_{texture}\left( {r_{i},r_{j}} \right)}} + {a_{3}{s_{size}\left( {r_{i},r_{j}} \right)}} + {a_{4}{{fill}\left( {r_{i},r_{j}} \right)}}} \\{{s_{color}\left( {r_{i},r_{j}} \right)} =} \\{{\sum\limits_{k = 1}^{n}{\min \left( {c_{i}^{k},c_{j}^{k}} \right)}},{{c_{i}^{k} \in C_{i}} = \left\{ {c_{i}^{1},\ldots \mspace{14mu},c_{i}^{n}} \right)},{{c_{j}^{k} \in C_{j}} = \left\{ {c_{j}^{1},\ldots \mspace{14mu},c_{j}^{n}} \right\}}} \\{{s_{texture}\left( {r_{i},r_{j}} \right)} =} \\{{\sum\limits_{k = 1}^{n}{\min \left( {t_{i}^{k},t_{j}^{k}} \right)}},{{t_{i}^{k} \in T_{i}} = \left\{ {c_{i}^{1},\ldots \mspace{14mu},c_{i}^{n}} \right\}},{{t_{j}^{k} \in T_{j}} = \left\{ {c_{j}^{1},\ldots \mspace{14mu},c_{j}^{n}} \right\}}} \\{{s_{size}\left( {r_{i},r_{j}} \right)} = {1 - \frac{{{size}\left( r_{i} \right)} + {{size}\left( r_{j} \right)}}{{size}({im})}}} \\{{{fill}\left( {r_{i},r_{j}} \right)} = {1 - \frac{{{size}\left( {BB}_{ij} \right)} - {{size}\left( r_{i} \right)} - {{size}\left( r_{j} \right)}}{{size}({im})}}}\end{matrix};} \right.$

Wherein s(r_(i),r_(j)), s_(color)(r_(i),r_(j)),s_(texture)(r_(i),r_(j)), s_(size)(r_(i),r_(j)) and fill(r_(i),r_(j))refer to comprehensive similarity, Color similarity, Texture similarity,Size similarity and Fill similarity respectively between a region r_(i)and a region r_(j), a₁, a₂, a₃ and a₄ refer to a Color similarityweighting coefficient, a Texture similarity weighting coefficient, aSize similarity weighting coefficient and a Fill similarity weightingcoefficient respectively, the value range of a₁, a₂, a₃ and a₄ is within(0, 1), C_(i)={c_(i) ¹, . . . , c_(i) ^(n)} and C_(j){c_(j) ¹, . . . ,c_(j) ^(n)} refer to 3×25-dimensional color space vectors correspondingto the region r_(i) and the region r_(j) respectively, T_(i)={t_(i) ¹, .. . , t_(i) ^(n)} and T_(j)={t_(j) ¹, . . . , t_(j) ^(n)} refer to8×3×10-dimensional texture vector corresponding to the region r_(i) andthe region r_(j) respectively, n refers to the total number of elementsof the color space vector or texture vector, and size(r_(i)),size(r_(j)), size(im) and size(BB_(ij)) refer to size of the regionr_(i), size of the region r_(j), size of the whole image combined by allthe regions, and size of a outer boundary rectangle of a region combinedby the region and the region, respectively; and

-   -   a region combination unit, configured to combine the initial        target regions according to the comprehensive similarity        calculated between the adjacent regions to obtain the set of        target regions, taking the comprehensive similarity maximum as a        combination principle.

The invention will be further described in detail hereinafter withreference to the drawings and specific embodiments of the description.

Embodiment I

The vehicles in type can be roughly divided into the large vehicles andsmall vehicles. The vehicle logos of the small vehicle are usually asymbol type vehicle logo, but the vehicle logos of the large vehiclescan be divided into three types according to the texture of the vehiclelogo, including a character type vehicle logo, a symbol type vehiclelogo and a mixed type vehicle logo, as shown in FIGS. 2A-2D, whereinFIG. 2A shows the character type vehicle logo, FIG. 2B shows the symboltype vehicle logo, FIG. 2C and FIG. 2D show the mixed type vehicle logo.Both the spatial structure and texture feature of the vehicle logos ofthe two types of vehicles are different. These factors increase thedifficulty in detection of vehicle logo.

To address the problem that the existing vehicle logo detecting andrecognizing method has narrow applicability, weak robustness and slowdetection speed, the present invention proposes a brand-new vehicle logodetection and recognition method based on Selective Search, which isapplicable for all vehicle models in vehicle logo detection. In order toensure real-time capability of vehicle logo positioning andeffectiveness of vehicle logo positioning under a complicated backgroundof vehicle logo, in the invention firstly the spatial relationshipbetween the license plate and the vehicle logo, and the vehicle windowedge feature are utilized to coarsely position the vehicle logo. Inaddition, in order to reduce the amount of calculations, in theinvention the central axis of the vehicle is utilized to obtain thevehicle logo candidate area. Moreover, the Selective Search is utilizedto perform target positioning and the ScSPM is used to perform targetscreening. Finally, the position of the vehicle logo and the vehiclelogo recognition result are obtained in a way of non-maximum suppression(namely convergence).

As shown in FIG. 1, a method of detecting and recognizing a vehicle logobased on Selective Search according to the invention has the specificprocess as follows.

(I) A license plate is positioned on an original vehicle image, toobtain a position of the license plate.

After the original vehicle image is captured, the position of thelicense plate can be obtained by using an existing license platepositioning method.

(II) The vehicle logo is coarsely positioned according to the positionof the license plate, the spatial structure between the license plateand the vehicle logo, and the vehicle window edge feature in theoriginal vehicle image, to obtain a coarse positioning image of thevehicle logo.

There is extremely important spatial structure among the vehicle logo,the license plate and the vehicle window: the vehicle logo is locatedabove the license plate, and the vehicle logo is located below thevehicle window. Under the condition of failing to determine the accuracyof the position information of the license plate, according to theinvention the coarse positioning position information of the licenseplate and the vehicle window edge feature are used to obtain the coarsepositioning position of the vehicle logo.

(1) A position of a down boundary of vehicle logo coarse positioning isdetermined according to a position of an up boundary of vehicle logocoarse positioning.

Y _(down) =y _(up)  (1)

wherein, Y_(down) denotes a coordinate of the down boundary of vehiclelogo coarse positioning, and y_(up) denotes a coordinate of the upboundary of license plate coarse positioning.

(2) The position of the up boundary of vehicle logo coarse positioningis determined by the vehicle window edge feature.

Edge and spatial position features of the vehicle window are obvious:the boundary edge of the vehicle window is complicated but the interioredge of it is relatively smooth, and the vehicle window is located at anuppermost portion of the front of the vehicle. Therefore, the vehiclewindow can be coarsely positioned by the vertical projection of thevehicle.

x ₁ ,x ₂=max₂ h(x)

s.t.|x ₁ −x ₂ |ϵ[H/4,H/2],x ₁ϵ(0,H/3],h(x ₁)−h(x ₂)≤b  (2)

wherein, h(x) denotes the vertical projection of a vehicle edge image,max₂ h(x) denotes x-coordinates x₁ and x₂ corresponding to two maximumvalues selected from the vertical projection of the edge from up todown, H denotes a height of the original vehicle image, and b refers toan experience threshold value.

The coordinate Y_(up) of the up boundary of vehicle logo coarsepositioning is:

Y _(up) =x ₂−(x ₂ −x ₁)/2  (3)

(3) The vehicle logo coarse positioning image can be obtained accordingto formulas (1), (2) and (3), as shown in FIGS. 3A-3B, wherein, FIG. 3Ais the original vehicle image, FIG. 3B is the vehicle logo coarsepositioning image based on the position of the license plate, and FIG.3C is a final vehicle logo coarse positioning image.

(III) A vehicle logo candidate area is selected from the vehicle logocoarse positioning image based on a central axis of vehicle.

Time consumption of the Selective Search is linearly related to thenumber of region pixels. Therefore, in order to reduce the timeconsumption, according to the invention, vehicle logo positioning areais reduced as much as possible while the integrity of the vehicle logois ensured. The vehicle logo candidate area is obtained according to thecentral axis of the vehicle in the invention, as shown in FIGS. 4A-4B,wherein, FIG. 4A is a schematic diagram of classifying the vehicle logocandidate area of the vehicle logo of a large vehicle, and FIG. 4B is aschematic diagram of classifying the vehicle logo candidate of thevehicle logo of a small vehicle.

A first vehicle logo candidate area 1 is selected by means of thecentral axis of the vehicle, and a width and a height of the firstvehicle logo candidate area can be set according to the actualconditions. The width of the first vehicle logo candidate area 1 in theembodiment is set as 64 pixels, and the height thereof is identical tothe height of the vehicle logo coarse positioning image. When in thefirst vehicle logo candidate area 1 a single character is detected, 1.5times of the height of the character is taken as a height of a newcandidate region (namely vehicle logo candidate area 3) and detectionfor the vehicle logo is continued; when a character string vehicle logoor a symbol type vehicle logo is detected, the calculation of theSelective Search is finished; when no target is detected, the candidateregion is transversely amplified (32 pixels amplified leftward andrightward, respectively) to form a second vehicle logo candidate area 2,and the detection for the vehicle logo is continued, if still no targetis detected, the first vehicle logo candidate area 1 is abandoned, andthe second vehicle logo candidate area 2 is continuously amplifiedoutward for vehicle logo detection. The time consumption of theSelective Search is greatly reduced by the candidate region selectingmechanism.

(IV) Target positioning on the vehicle logo candidate area is performedwith the Selective Search to obtain the set of target regions.

The Selective Search is an algorithm based on region combination, itscalculation time is linearly related to the number of the pixel dots inthe detection region, and its target area is obtained throughcombination of similar regions. Original segmentation regions areobtained by using an image segmentation algorithm based on figures,e.g., histogram of oriented gradients, in the present invention,similarity between two adjacent regions is calculated, the two regionshaving the maximum similarity are combined into one region, and thesimilarity between two adjacent regions are recalculated until an entireimage (namely the target result set) is finally combined. The flow chartof the Selective Search is as shown in FIG. 5.

The similarity of the adjacent regions is calculated by the SelectiveSearch in terms of color, texture, size and fitness, in the invention.

(1) Color Similarity

The imaged is normalized, and a histogram of 25 regions of each colorchannel of the image, i.e. a 3*25-dimensional color space vectorC_(i)={c_(i) ¹, . . . , c_(i) ^(n)}, is obtained in each segmentationregion, the similarity s_(color)(r_(i),r_(j)) between the regions iscalculated by a formula:

$\begin{matrix}{{{s_{color}\left( {r_{i},r_{j}} \right)} = {\sum\limits_{k = 1}^{n}{\min \left( {c_{i}^{k},c_{j}^{k}} \right)}}},{{c_{i}^{k} \in C_{i}} = \left\{ {c_{i}^{1},\ldots \mspace{14mu},c_{i}^{n}} \right)},{{c_{j}^{k} \in C_{j}} = \left\{ {c_{j}^{1},\ldots \mspace{14mu},c_{j}^{n}} \right\}}} & (4)\end{matrix}$

The histogram needs to be recalculated for new regions in the regioncombination process through the simplified algorithm of the formula (5):

$\begin{matrix}{C_{t} = \frac{{{{size}\left( r_{i} \right)} \times C_{i}} + {{{size}\left( r_{j} \right)} \times C_{j}}}{{{size}\left( r_{i} \right)} + {{size}\left( r_{j} \right)}}} & (5)\end{matrix}$

size(r_(i)) denotes the size of the region r_(i), size(r_(j)) denotesthe size of the region r_(j), C_(i) and C_(j) denote the color vectorsof the regions r_(i) and r_(j), C_(i) and refers to the color vector ofa new region.

(2) Texture Similarity

In order to reduce the amount of calculation, HOG feature is adopted inthe invention in replace of SIFT feature to describe the region texture.Gradient feature is counted through a gradient histogram of 8 regions inthe invention, 10 spaces are acquired in each color channel of theregion, i.e., a 8*3*10 vector quantity T_(i)={t_(i) ¹, . . . , t_(i)^(n)} is obtained in each region, then a calculation formula of theTexture similarity s_(texture)(r_(i),r_(j)) between the regions is:

$\begin{matrix}{{{s_{texture}\left( {r_{i},r_{j}} \right)} = {\sum\limits_{k = 1}^{n}{\min \left( {t_{i}^{k},t_{j}^{k}} \right)}}},{{t_{i}^{k} \in T_{i}} = \left\{ {c_{i}^{1},\ldots \mspace{14mu},c_{i}^{n}} \right\}},{{t_{j}^{k} \in T_{j}} = \left\{ {c_{j}^{1},\ldots \mspace{14mu},c_{j}^{n}} \right\}}} & (6)\end{matrix}$

(3) Size Similarity

When the Color similarity and the Texture similarity between twoadjacent regions are the same, it shall be ensured that the regionshaving smaller areas are combined first, i.e., the regions having largerSize similarity are combined first, and the Size similarity iss_(size)(r_(i),r_(j)) is therefore defined in the invention as:

$\begin{matrix}{{s_{size}\left( {r_{i},r_{j}} \right)} = {1 - \frac{{{size}\left( r_{i} \right)} + {{size}\left( r_{j} \right)}}{{size}({im})}}} & (7)\end{matrix}$

In the above formula, size(im) denotes the size of the entire imageobtained after all regions are completely combined.

(4) Fill Similarity

In order to reduce the amount of calculations, the regions having acrossing or inclusion relation are first combined in the invention, andthe Fill similarity is defined as:

$\begin{matrix}{{{fill}\left( {r_{i},r_{j}} \right)} = {1 - \frac{{{size}\left( {BB}_{ij} \right)} - {{size}\left( r_{i} \right)} - {{size}\left( r_{j} \right)}}{{size}({im})}}} & (8)\end{matrix}$

wherein, size(BB_(ij)) denotes the size of the outer boundary rectangleof the region combined by the region r_(i) and the region r_(j).

The above four similarities are combined in a way of weighted summationto obtain a comprehensive similarity between the adjacent regions.

s(r _(i) ,r _(j))=a ₁ s _(color) +a ₂ s _(texture) +a ₃ s _(size) +a₄fill  (9)

The initial target regions are combined through the formulas (4) to (9).In this way, not only the different sizes can be adapted, but also thesegmentation accuracy of the target can be ensured.

(V) Judgement of Vehicle Logo and Recognition of Vehicle Logo Type

With the Selective Search, only the target regions can be positioned, itcannot be determined that whether a region positioned is the vehiclelogo region. Therefore, according to the invention, the Spatial PyramidMatching based on Sparse Coding (ScSPM) is used to train the vehiclelogo location classifier to determine the vehicle logo from the set oftarget regions as well as to train the vehicle logo recognitionclassifier to recognize the specific type of the vehicle logo.

The ScSPM is a more mature classifier training algorithm, in whichfirstly SIFT (Scale Invariant Feature Transform) is used to extract thefeature of the target image, and then a pre-generated dictionary andlinear encoding is used to encode the target image, so as to construct areasonable image semantic expression model to express each image, andthen a spatial pyramid matching method is used to get the finalexpression vector of the image, and finally the final expression vectorof each image is fed into a Support Vector Machine (SVM) for trainingand recognition. The algorithm can be used for effectively training thevehicle logo location classifier and the vehicle logo recognitionclassifier.

Wherein, a training process of the vehicle logo location classifier isas shown in FIG. 6: the large vehicle and small vehicle in a sample setare taken as positive samples; in addition, single characters are addedas positive samples, and negative samples are samples havingintersection over union (IoU) less than 20% with the vehicle logo andrandomly selected in size as all characters in a character stringvehicle logo are easy to be dispersedly positioned; and then thepositive samples are taken as training samples, the negative samples aretaken as test samples, and the vehicle logo location classifier istrained using the ScSPM. With the vehicle logo location classifier, in away of iteration training, while each training is completed, thesamples, which are wrongly divided into the negative samples in thevehicle logo location classifier, i.e. hard examples, are added into thetraining samples, and then trained using the ScSPM again untilconvergence. By means of the above process, in the invention the vehiclelogo location classifier is obtained by training, and the vehicle logocan be determined according to the vehicle logo location classifier.While a plurality of vehicle logos are detected for a vehicle, in theinvention the vehicle logo location classifier can be used to score eachof the vehicle logos, and consequently to provide target vehicle logopositioning results according to the scores.

But for a part of the character string vehicle logos, due to large gapsbetween characters, a character string cannot be positioned completely,and only a single character in the character string can be positioned,so that the type of the vehicle logo cannot be recognized. Directed tothis problem, according to the invention the ScSPM is used to train amulti-class vehicle logo recognition classifier, as shown in FIG. 7.

As shown in FIG. 7, single characters are taken as the vehicle logo intraining when the multi-class vehicle logo recognition classifier isadopted, and as the total characters are relatively few, the recognitioneffect of the classifier will not be affected. In the invention, thesingle characters of the manually annotated vehicle logo and thecharacter string vehicle logo are fed into the ScSPM to perform trainingiteratively, and the wrongly-classified vehicle logos as the hardexamples ae fed into the ScSPM again for training until convergence. Dueto the nature of the ScSPM, basically, training iterations of threetimes can achieve convergence. A specific vehicle logo recognitionprocess of the invention is as shown in FIG. 8: if a vehicle logorecognition result of the multi-class vehicle logo recognitionclassifier is a vehicle logo of a kind of non-single-character, thevehicle logo of the vehicle can be determined according to therecognition result; if the vehicle logo recognition result of themulti-class vehicle logo recognition classifier is a single character,the third vehicle logo candidate area 3 needs to be reselected accordingto the character, and the vehicle logo is repositioned, and thecharacters obtained by repositioning are combined to form a characterstring, so as to obtain a final vehicle logo recognition result.

The present invention provides a novel vehicle logo detecting andrecognizing method based on Selective Search, and consequently has thefollowing advantages.

1) It is applicable to all types of vehicle models and is not limited bythe vehicle models, and large or small vehicles can be positioned.

2) The Selective Search is performed by the texture, color, size andfitness features of the vehicle logo, so that the position of thevehicle logo can be positioned correctly.

3) The vehicle logo location classifier and the vehicle logo recognitionclassifier are trained using the excellent feature of the SpatialPyramid Matching based on Sparse Coding, which covers the vast majorityof vehicle logo types, and can more accurately and effectively positionand recognize the vehicle logo.

4) The vehicle logo candidate area is selected according to the spaceposition information and the central axis of the vehicle, which greatlyreduces the time consumption of the Selective Search, has a fasterdetection speed, so that the requirement for the real-time capability ofpositioning the vehicle logo is satisfied.

5) A new Texture similarity calculating method is defined, and thegradient histogram is used to replace the scale invariant transformationfeature, which can greatly reduce the time consumption while ensuringthe positioning accuracy rate and recognition accuracy rate of thevehicle logo.

6) The computational complexity is far lower than the existing vehiclelogo detection and recognition technology, which is a high-speed andeffective vehicle logo accurate positioning and identification scheme.

The above is the specific description for the preferred embodiment ofthe present invention, but the prevent invention is not intended tolimit the foregoing embodiments. Various identical transformations orreplacements can further be made by those skilled in the art withoutdeparting from the spirit of the invention shall all fall within thescope limited by the claims.

We claim:
 1. A method of detecting and recognizing a vehicle logo basedon Selective Search, comprising the steps of: positioning a licenseplate on an original image of a vehicle to obtain a license plateposition; coarsely positioning a vehicle logo on the original image ofthe vehicle according to the license plate position, spatial structurebetween the license plate and the vehicle logo, and vehicle window edgefeature, to obtain a coarse positioning image of the vehicle logo;selecting vehicle logo candidate areas in the coarse positioning imageof the vehicle logo based on a central axis of the vehicle; performingtarget positioning on the vehicle logo candidate areas with SelectiveSearch, to obtain a set of target regions, wherein region combinationperformed with the Selective Search is based on color similarity,texture similarity, size similarity and fill similarity comprehensively;training a vehicle logo location classifier with Spatial PyramidMatching based on Sparse Coding (ScSPM), to determine the vehicle logofrom the set of target regions, to obtain a vehicle logo position; andtraining a multi-class vehicle logo recognition classifier with theSpatial Pyramid Matching based on Sparse Coding (ScSPM) to conductspecific type-recognition for the vehicle logo, to obtain a vehicle logorecognition result.
 2. The method of claim 1, wherein the step ofcoarsely positioning a vehicle logo further comprises the steps of:determining a position of a down boundary for vehicle logo coarsepositioning according to a position of an up boundary for vehicle logocoarse positioning, wherein the coordinate of the down boundary,Y_(down), is determined by a formula Y_(down)=y_(up), and y_(up) refersto the coordinate of the up boundary; coarsely positioning a vehiclewindow according to vertical projection of the original image of thevehicle, to obtain the vehicle window edge feature, and determining aposition of the up boundary according to the vehicle window edgefeature, the coordinate Y_(up) of the up boundary being determined by aformula: $\left\{ {\begin{matrix}{Y_{up} = {x_{2} - {\left( {x_{2} - x_{1}} \right)/2}}} \\{x_{1},{x_{2} = {\max_{2}{h(x)}}}} \\{{{s.t.\mspace{14mu} {{x_{1} - x_{2}}}} \in \left\lbrack {{H/4},{H/2}} \right\rbrack},{x_{1} \in \left( {0,{H/3}} \right\rbrack},{{{{h\left( x_{1} \right)} - {h\left( x_{2} \right)}}} \leq b}}\end{matrix},} \right.$ wherein, h(x) refers to the vertical projectionof the original image of the vehicle, max₂ h(x) denotes x-coordinates x₁and x₂ corresponding to two maximum values h(x₁) and h(x₂) selected fromthe vertical projection h(x) of the edge from up to down, H refers to aheight of the original image of the vehicle, and b refers to anexperience threshold value; and obtaining the coarse positioning imageof the vehicle logo according to the coordinate Y_(down) of downboundary and the coordinate Y_(up) of the up boundary.
 3. The method ofclaim 1, wherein the step of selecting vehicle logo candidate areasfurther comprises the steps of: determining the central axis of thevehicle on the coarse positioning image of the vehicle logo; andselecting an area having a set width and a set height as a first vehiclelogo candidate area according to the central axis of the vehicle.
 4. Themethod of claim 3, wherein the step of performing target positioningfurther comprises the steps of: performing target detection in the firstvehicle logo candidate area using the Selective Search; and determiningif the target detected is a single character or a character string logoor a symbol logo.
 5. The method of claim 4, wherein the step ofperforming target positioning further comprises the step of transverselyamplifying the first vehicle logo candidate area with a transverselyamplified width set to form a second vehicle logo candidate area, andthen performing target detection for vehicle logo in the second vehiclelogo candidate area using the Selective Search.
 6. The method of claim4, wherein the step of performing target positioning further comprisesthe step of abandoning the first vehicle logo candidate area andcontinuing to amplify the second vehicle logo candidate area with a newtransversely amplified width, until a target is detected in the secondvehicle logo candidate area.
 7. The method of claim 4, wherein the stepof performing target positioning further comprises the step of endingthe Selective Search if the target detected is at least one of thecharacter string logo or the symbol logo.
 8. The method of claim 4,wherein the step of performing target positioning further comprises thesteps of: taking 1.5 times of the character height as a height of thecandidate region, i.e. a height of the third vehicle logo candidatearea, if the target detected is the single character; and continuing toperform target detection in the third vehicle logo candidate area usingthe selectivity search.
 9. The method of claim 1, wherein the step ofperforming target positioning further comprises the steps of: obtaininginitial target regions from the vehicle logo candidate areas using animage segmentation algorithm based on a graph theory; calculatingsimilarity between the adjacent regions in the initial target regionsobtained, via a calculation formula: $\left\{ {\begin{matrix}{{s\left( {r_{i},r_{j}} \right)} =} \\{{a_{1}{s_{color}\left( {r_{i},r_{j}} \right)}} + {a_{2}{s_{texture}\left( {r_{i},r_{j}} \right)}} + {a_{3}{s_{size}\left( {r_{i},r_{j}} \right)}} + {a_{4}{{fill}\left( {r_{i},r_{j}} \right)}}} \\{{s_{color}\left( {r_{i},r_{j}} \right)} =} \\{{\sum\limits_{k = 1}^{n}{\min \left( {c_{i}^{k},c_{j}^{k}} \right)}},{{c_{i}^{k} \in C_{i}} = \left\{ {c_{i}^{1},\ldots \mspace{14mu},c_{i}^{n}} \right)},{{c_{j}^{k} \in C_{j}} = \left\{ {c_{j}^{1},\ldots \mspace{14mu},c_{j}^{n}} \right\}}} \\{{s_{texture}\left( {r_{i},r_{j}} \right)} =} \\{{\sum\limits_{k = 1}^{n}{\min \left( {t_{i}^{k},t_{j}^{k}} \right)}},{{t_{i}^{k} \in T_{i}} = \left\{ {c_{i}^{1},\ldots \mspace{14mu},c_{i}^{n}} \right\}},{{t_{j}^{k} \in T_{j}} = \left\{ {c_{j}^{1},\ldots \mspace{14mu},c_{j}^{n}} \right\}}} \\{{s_{size}\left( {r_{i},r_{j}} \right)} = {1 - \frac{{{size}\left( r_{i} \right)} + {{size}\left( r_{j} \right)}}{{size}({im})}}} \\{{{fill}\left( {r_{i},r_{j}} \right)} = {1 - \frac{{{size}\left( {BB}_{ij} \right)} - {{size}\left( r_{i} \right)} - {{size}\left( r_{j} \right)}}{{size}({im})}}}\end{matrix};} \right.$ wherein s(r_(i),r_(j)), s_(color)(r_(i),r_(j)),s_(texture)(r_(i),r_(j)), s_(size)(r_(i),r_(j)) and fill(r_(i),r_(j))refer to comprehensive similarity, color similarity, texture similarity,size similarity and fill similarity respectively between a region r_(i)and a region r_(j), a₁, a₂, a₃ and a₄ refer to a color similarityweighting coefficient, a texture similarity weighting coefficient, asize similarity weighting coefficient and a fill similarity weightingcoefficient respectively, the value range of a₁, a₂, a₃ and a₄ is within(0, 1), C_(i)={c_(i) ¹, . . . , c_(i) ^(n)} and C_(j)={c_(j) ¹, . . . ,c_(j) ^(n)} refer to 3×25-dimensional color space vectors correspondingto the region r_(i) and the region r_(j) respectively, T={t_(i) ¹, . . ., t_(i) ^(n)} and T_(j)={t_(j) ¹, . . . , t_(j) ^(n)} refer to8×3×10-dimensional texture vector corresponding to the region r_(i) andthe region r_(j) respectively, n refers to the total number of elementsof the color space vector or texture vector, and size(r_(i)),size(r_(j)) size(im) and size(BB_(ij)) refer to size of the regionr_(i), size of the region r_(j), size of the whole image combined by allthe regions, and size of an outer boundary rectangle of a regioncombined by the region r_(i) and the region r_(j), respectively; andtaking the comprehensive similarity maximum as a combination principle,combining the initial target regions according to the comprehensivesimilarity calculated between the adjacent regions, to obtain the set oftarget regions.
 10. The method according to claim 1, wherein the step oftraining a vehicle logo location classifier further comprises the stepsof: dividing sample images into positive samples and negative samples,wherein the positive samples comprises single character samples, andsmall-vehicle logo samples and large-vehicle logo samples in the sampleset, and the negative samples are samples having intersection over union(IoU) 20% less than ground truth and having random sizes; taking thepositive samples as training samples, and using Spatial Pyramid Matchingbased on Sparse Coding (ScSPM) to perform training iteratively untilconvergence, and finally to have the vehicle logo location classifier,wherein, in the iterative training process, after each training, thesamples which are wrongly detected into the negative samples in thevehicle logo location classifier is added into the training samples toform a new training sample set, then the new training sample set is usedfor retraining; and determining the vehicle logo from the set of targetregions according to the vehicle logo location classified trained, toobtain the position of the vehicle logo.
 11. The method according toclaim 4, wherein the step of training a multi-class vehicle logorecognition classifier further comprises the steps of: taking singlecharacters as vehicle logos, feeding the single characters of manuallyannotated vehicle logos and the character string vehicle logos into theSpatial Pyramid Matching based on Sparse Coding (ScSPM) to performtraining iteratively; taking the wrongly-classified vehicle logos ashard examples and feeding them into the Spatial Pyramid Matching basedon Sparse Coding (ScSPM) for training until convergence, to obtain themulti-class vehicle logo recognition classifier; and performing specifictype-recognition for the vehicle logo with the multi-class vehicle logorecognition classifier by taking the result as the vehicle logo type, ifthe vehicle logo recognition result is a letter logo or symbol;combining the characters obtained by positioning to form the letterlogo; and recognizing the letter logo.
 12. The method according to claim4, wherein the step of training a multi-class vehicle logo recognitionclassifier further comprises the steps of: taking single characters asvehicle logos, feeding the single characters of manually annotatedvehicle logos and the character string vehicle logos into the SpatialPyramid Matching based on Sparse Coding (ScSPM) to perform trainingiteratively, and taking the wrongly-classified vehicle logos as hardexamples and feeding them into the Spatial Pyramid Matching based onSparse Coding (ScSPM) for training until convergence, to obtain themulti-class vehicle logo recognition classifier; and performing specifictype-recognition for the vehicle logo with the multi-class vehicle logorecognition classifier by detecting the vehicle logo in a thirdcandidate area, if the vehicle logo recognition result is a singlecharacter; combining the characters obtained by positioning to form theletter logo; and recognizing the letter logo.
 13. A system of detectingand recognizing a vehicle logo based on Selective Search, comprising: alicense plate positioning module, configured to position a license plateon an original image of a vehicle, to obtain a license plate position; avehicle logo coarsely positioning module, configured to coarselyposition a vehicle logo on the original image of the vehicle accordingto the license plate position, spatial structure between the licenseplate and the vehicle logo, and vehicle window edge feature, to obtain acoarse positioning image of the vehicle logo; a vehicle logo candidatearea selecting module, configured to select vehicle logo candidate areasin the coarse positioning image of the vehicle logo based on a centralaxis of the vehicle; a target positioning module, configured to performtarget positioning in the vehicle logo candidate areas with SelectiveSearch, to obtain a set of target regions, and performing regioncombination with the Selective Search based on color similarity, texturesimilarity, size similarity and fill similarity comprehensively; avehicle logo determining module, configured to train a vehicle logolocation classifier with Spatial Pyramid Matching based on Sparse Coding(ScSPM), to determine the vehicle logo from the set of target regions,to obtain a vehicle logo position; and a vehicle logo type recognitionmodule, configured to train a multi-class vehicle logo recognitionclassifier with the Spatial Pyramid Matching based on Sparse Coding(ScSPM) to conduct a specific type-recognition for the vehicle logo, toobtain a vehicle logo recognition result.
 14. The system of claim 13,wherein the vehicle logo coarse positioning module comprises: a vehiclelogo coarse positioning down boundary position determining unit,configured to determine a position of a down boundary for vehicle logocoarse positioning according to a position of a up boundary for vehiclelogo coarse positioning, wherein the coordinate Y_(down) of the downboundary is determined by a formula Y_(down)=y_(up), wherein y_(up)refers to the coordinate of the up boundary; a vehicle logo coarsepositioning up boundary position determining unit, configured tocoarsely position a vehicle window according to vertical projection ofthe original image of the vehicle to obtain the vehicle window edgefeature, and determining a position of the up boundary according to thevehicle window edge feature, the coordinate Y_(up) of the up boundarybeing determined by a formula: $\left\{ {\begin{matrix}{Y_{up} = {x_{2} - {\left( {x_{2} - x_{1}} \right)/2}}} \\{x_{1},{x_{2} = {\max_{2}{h(x)}}}} \\{{{s.t.\mspace{14mu} {{x_{1} - x_{2}}}} \in \left\lbrack {{H/4},{H/2}} \right\rbrack},{x_{1} \in \left( {0,{H/3}} \right\rbrack},{{{{h\left( x_{1} \right)} - {h\left( x_{2} \right)}}} \leq b}}\end{matrix},} \right.$ wherein, h(x) refers to the vertical projectionof the original image of the vehicle, max₂ h(x) denotes x-coordinates x₁and x₂ corresponding to two maximum values h(x₁) and h(x₂) selected fromthe vertical projection h(x) of the edge from up to down, H refers to aheight of the original image of the vehicle, and b refers to anexperience threshold value; and a vehicle logo coarse positioning imageobtaining unit, configured to obtain the coarse positioning image of thevehicle logo according to the coordinate Y_(down) of down boundary andthe coordinate Y_(up) of the up boundary.
 15. The system of claim 13,wherein the target positioning module comprises: an image segmentationunit, configured to obtain initial target regions from the vehicle logocandidate areas using an image segmentation algorithm based on a graphtheory; a similarity calculation unit, configured to calculatesimilarity between the adjacent regions in the initial target regions,via a calculation formula: $\left\{ {\begin{matrix}{{s\left( {r_{i},r_{j}} \right)} =} \\{{a_{1}{s_{color}\left( {r_{i},r_{j}} \right)}} + {a_{2}{s_{texture}\left( {r_{i},r_{j}} \right)}} + {a_{3}{s_{size}\left( {r_{i},r_{j}} \right)}} + {a_{4}{{fill}\left( {r_{i},r_{j}} \right)}}} \\{{s_{color}\left( {r_{i},r_{j}} \right)} =} \\{{\sum\limits_{k = 1}^{n}{\min \left( {c_{i}^{k},c_{j}^{k}} \right)}},{{c_{i}^{k} \in C_{i}} = \left\{ {c_{i}^{1},\ldots \mspace{14mu},c_{i}^{n}} \right)},{{c_{j}^{k} \in C_{j}} = \left\{ {c_{j}^{1},\ldots \mspace{14mu},c_{j}^{n}} \right\}}} \\{{s_{texture}\left( {r_{i},r_{j}} \right)} =} \\{{\sum\limits_{k = 1}^{n}{\min \left( {t_{i}^{k},t_{j}^{k}} \right)}},{{t_{i}^{k} \in T_{i}} = \left\{ {c_{i}^{1},\ldots \mspace{14mu},c_{i}^{n}} \right\}},{{t_{j}^{k} \in T_{j}} = \left\{ {c_{j}^{1},\ldots \mspace{14mu},c_{j}^{n}} \right\}}} \\{{s_{size}\left( {r_{i},r_{j}} \right)} = {1 - \frac{{{size}\left( r_{i} \right)} + {{size}\left( r_{j} \right)}}{{size}({im})}}} \\{{{fill}\left( {r_{i},r_{j}} \right)} = {1 - \frac{{{size}\left( {BB}_{ij} \right)} - {{size}\left( r_{i} \right)} - {{size}\left( r_{j} \right)}}{{size}({im})}}}\end{matrix};} \right.$ wherein s(r_(i),r_(j)), s_(color)(r_(i),r_(j)),s_(texture)(r_(i),r_(j)), s_(size)(r_(i),r_(j)) and fill(r_(i),r_(j))refer to similarity, color similarity, texture similarity, sizesimilarity and fill similarity respectively between a region r_(i) and aregion r_(j), a₁, a₂, a₃ and a₄ refer to a color similarity weightingcoefficient, a Texture similarity weighting coefficient, a sizesimilarity weighting coefficient and a fill similarity weightingcoefficient respectively, the value range of a₁, a₂, a₃ and a₄ is within(0, 1), C_(i)={c_(i) ¹, . . . , c_(i) ^(n)} and C_(j)={c_(j) ¹, . . . ,c_(j) ^(n)} refer to 3×25-dimensional color space vectors correspondingto the region r_(i) and the region r_(j) respectively, T_(i)={t_(i) ¹, .. . , t_(i) ^(n)} and T_(j)={t_(j) ¹, . . . , t_(n) ^(j)} refer to8×3×10-dimensional texture vector corresponding to the region r_(i) andthe region r_(j) respectively, n refers to the total number of elementsof the color space vector or texture vector, and size(r_(i)),size(r_(j)) size(im) and size(BB_(ij)) refer to size of the regionr_(i), size of the region r_(j), size of the whole image combined by allthe regions, and size of a outer boundary rectangle of a region combinedby the region r_(i) and the region r_(j), respectively; and a regioncombination unit, configured to combine the initial target regionsaccording to the similarity calculated between the adjacent regions toobtain the set of target regions, taking the similarity maximum as acombination principle.